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{
"cells": [
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"from transformers import GPT2LMHeadModel, GPT2Tokenizer\n",
"import torch\n",
"DEVICE = torch.device(\"cuda:0\")\n",
"\n",
"# Для большей наглядности будем работать с русскоязычной версией GPT от Сбера.\n",
"# Ниже находятся команды для загрузки и инициализации модели и токенизатора.\n",
"model_name_or_path = \"sberbank-ai/rugpt3small_based_on_gpt2\"\n",
"tokenizer = GPT2Tokenizer.from_pretrained(model_name_or_path)\n",
"model = GPT2LMHeadModel.from_pretrained(model_name_or_path).to(DEVICE)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/danil-pc/miniforge3/envs/st_image/lib/python3.10/site-packages/transformers/data/datasets/language_modeling.py:53: FutureWarning: This dataset will be removed from the library soon, preprocessing should be handled with the 🤗 Datasets library. You can have a look at this example script for pointers: https://github.com/huggingface/transformers/blob/main/examples/pytorch/language-modeling/run_mlm.py\n",
" warnings.warn(\n"
]
}
],
"source": [
"from transformers import TextDataset, DataCollatorForLanguageModeling\n",
"\n",
"# Сохраним обучающие данные в .txt файл \n",
"train_path = 'punkt_texts.txt'\n",
"# Создание датасета\n",
"train_dataset = TextDataset(tokenizer=tokenizer,file_path=train_path,block_size=64)\n",
" \n",
"# Создание даталодера (нарезает текст на оптимальные по длине куски)\n",
"data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [],
"source": [
"from transformers import Trainer, TrainingArguments\n",
"\n",
"training_args = TrainingArguments(\n",
" output_dir=\"./finetuned\", #The output directory\n",
" overwrite_output_dir=True, #overwrite the content of the output directory\n",
" num_train_epochs=1, # number of training epochs\n",
" per_device_train_batch_size=16, # batch size for training\n",
" per_device_eval_batch_size=16, # batch size for evaluation\n",
" warmup_steps=10,# number of warmup steps for learning rate scheduler\n",
" gradient_accumulation_steps=16, # to make \"virtual\" batch size larger\n",
" )\n",
"\n",
"\n",
"trainer = Trainer(\n",
" model=model,\n",
" args=training_args,\n",
" data_collator=data_collator,\n",
" train_dataset=train_dataset,\n",
" optimizers = (torch.optim.AdamW(model.parameters(),lr=1e-5),None) # Optimizer and lr scheduler\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
" 5%|▌ | 1/20 [00:39<12:33, 39.66s/it]\n",
" 25%|██▌ | 1/4 [00:07<00:22, 7.52s/it]"
]
},
{
"ename": "OutOfMemoryError",
"evalue": "CUDA out of memory. Tried to allocate 194.00 MiB. GPU 0 has a total capacity of 3.80 GiB of which 93.19 MiB is free. Including non-PyTorch memory, this process has 3.10 GiB memory in use. Of the allocated memory 2.92 GiB is allocated by PyTorch, and 103.24 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mOutOfMemoryError\u001b[0m Traceback (most recent call last)",
"Cell \u001b[0;32mIn[9], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mtrainer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtrain\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/transformers/trainer.py:2123\u001b[0m, in \u001b[0;36mTrainer.train\u001b[0;34m(self, resume_from_checkpoint, trial, ignore_keys_for_eval, **kwargs)\u001b[0m\n\u001b[1;32m 2121\u001b[0m hf_hub_utils\u001b[38;5;241m.\u001b[39menable_progress_bars()\n\u001b[1;32m 2122\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 2123\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43minner_training_loop\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 2124\u001b[0m \u001b[43m \u001b[49m\u001b[43margs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2125\u001b[0m \u001b[43m \u001b[49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mresume_from_checkpoint\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2126\u001b[0m \u001b[43m \u001b[49m\u001b[43mtrial\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrial\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2127\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mignore_keys_for_eval\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 2128\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/transformers/trainer.py:2481\u001b[0m, in \u001b[0;36mTrainer._inner_training_loop\u001b[0;34m(self, batch_size, args, resume_from_checkpoint, trial, ignore_keys_for_eval)\u001b[0m\n\u001b[1;32m 2475\u001b[0m context \u001b[38;5;241m=\u001b[39m (\n\u001b[1;32m 2476\u001b[0m functools\u001b[38;5;241m.\u001b[39mpartial(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maccelerator\u001b[38;5;241m.\u001b[39mno_sync, model\u001b[38;5;241m=\u001b[39mmodel)\n\u001b[1;32m 2477\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m i \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(batch_samples) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m\n\u001b[1;32m 2478\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m contextlib\u001b[38;5;241m.\u001b[39mnullcontext\n\u001b[1;32m 2479\u001b[0m )\n\u001b[1;32m 2480\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m context():\n\u001b[0;32m-> 2481\u001b[0m tr_loss_step \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtraining_step\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2483\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 2484\u001b[0m args\u001b[38;5;241m.\u001b[39mlogging_nan_inf_filter\n\u001b[1;32m 2485\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m is_torch_xla_available()\n\u001b[1;32m 2486\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m (torch\u001b[38;5;241m.\u001b[39misnan(tr_loss_step) \u001b[38;5;129;01mor\u001b[39;00m torch\u001b[38;5;241m.\u001b[39misinf(tr_loss_step))\n\u001b[1;32m 2487\u001b[0m ):\n\u001b[1;32m 2488\u001b[0m \u001b[38;5;66;03m# if loss is nan or inf simply add the average of previous logged losses\u001b[39;00m\n\u001b[1;32m 2489\u001b[0m tr_loss \u001b[38;5;241m=\u001b[39m tr_loss \u001b[38;5;241m+\u001b[39m tr_loss \u001b[38;5;241m/\u001b[39m (\u001b[38;5;241m1\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m-\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_globalstep_last_logged)\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/transformers/trainer.py:3579\u001b[0m, in \u001b[0;36mTrainer.training_step\u001b[0;34m(self, model, inputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m 3576\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m loss_mb\u001b[38;5;241m.\u001b[39mreduce_mean()\u001b[38;5;241m.\u001b[39mdetach()\u001b[38;5;241m.\u001b[39mto(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mdevice)\n\u001b[1;32m 3578\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mcompute_loss_context_manager():\n\u001b[0;32m-> 3579\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcompute_loss\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43minputs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnum_items_in_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3581\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m inputs\n\u001b[1;32m 3582\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m (\n\u001b[1;32m 3583\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mtorch_empty_cache_steps \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 3584\u001b[0m \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstate\u001b[38;5;241m.\u001b[39mglobal_step \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mtorch_empty_cache_steps \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m\n\u001b[1;32m 3585\u001b[0m ):\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/transformers/trainer.py:3633\u001b[0m, in \u001b[0;36mTrainer.compute_loss\u001b[0;34m(self, model, inputs, return_outputs, num_items_in_batch)\u001b[0m\n\u001b[1;32m 3631\u001b[0m loss_kwargs[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnum_items_in_batch\u001b[39m\u001b[38;5;124m\"\u001b[39m] \u001b[38;5;241m=\u001b[39m num_items_in_batch\n\u001b[1;32m 3632\u001b[0m inputs \u001b[38;5;241m=\u001b[39m {\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39minputs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mloss_kwargs}\n\u001b[0;32m-> 3633\u001b[0m outputs \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43minputs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 3634\u001b[0m \u001b[38;5;66;03m# Save past state if it exists\u001b[39;00m\n\u001b[1;32m 3635\u001b[0m \u001b[38;5;66;03m# TODO: this needs to be fixed and made cleaner later.\u001b[39;00m\n\u001b[1;32m 3636\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39margs\u001b[38;5;241m.\u001b[39mpast_index \u001b[38;5;241m>\u001b[39m\u001b[38;5;241m=\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/transformers/models/gpt2/modeling_gpt2.py:1304\u001b[0m, in \u001b[0;36mGPT2LMHeadModel.forward\u001b[0;34m(self, input_ids, past_key_values, attention_mask, token_type_ids, position_ids, head_mask, inputs_embeds, encoder_hidden_states, encoder_attention_mask, labels, use_cache, output_attentions, output_hidden_states, return_dict)\u001b[0m\n\u001b[1;32m 1302\u001b[0m \u001b[38;5;66;03m# Flatten the tokens\u001b[39;00m\n\u001b[1;32m 1303\u001b[0m loss_fct \u001b[38;5;241m=\u001b[39m CrossEntropyLoss()\n\u001b[0;32m-> 1304\u001b[0m loss \u001b[38;5;241m=\u001b[39m \u001b[43mloss_fct\u001b[49m\u001b[43m(\u001b[49m\u001b[43mshift_logits\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshift_logits\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msize\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mshift_labels\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mview\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m-\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1306\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m return_dict:\n\u001b[1;32m 1307\u001b[0m output \u001b[38;5;241m=\u001b[39m (lm_logits,) \u001b[38;5;241m+\u001b[39m transformer_outputs[\u001b[38;5;241m1\u001b[39m:]\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/torch/nn/modules/module.py:1736\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1734\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs) \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m 1735\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1736\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/torch/nn/modules/module.py:1747\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1742\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m 1743\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m 1744\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m 1745\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m 1746\u001b[0m \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1747\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1749\u001b[0m result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 1750\u001b[0m called_always_called_hooks \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mset\u001b[39m()\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/torch/nn/modules/loss.py:1293\u001b[0m, in \u001b[0;36mCrossEntropyLoss.forward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m 1292\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor, target: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m-> 1293\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 1294\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1295\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1296\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1297\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1298\u001b[0m \u001b[43m \u001b[49m\u001b[43mreduction\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mreduction\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1299\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 1300\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n",
"File \u001b[0;32m~/miniforge3/envs/st_image/lib/python3.10/site-packages/torch/nn/functional.py:3479\u001b[0m, in \u001b[0;36mcross_entropy\u001b[0;34m(input, target, weight, size_average, ignore_index, reduce, reduction, label_smoothing)\u001b[0m\n\u001b[1;32m 3477\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m size_average \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m reduce \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 3478\u001b[0m reduction \u001b[38;5;241m=\u001b[39m _Reduction\u001b[38;5;241m.\u001b[39mlegacy_get_string(size_average, reduce)\n\u001b[0;32m-> 3479\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_C\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_nn\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcross_entropy_loss\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3480\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3481\u001b[0m \u001b[43m \u001b[49m\u001b[43mtarget\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3482\u001b[0m \u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3483\u001b[0m \u001b[43m \u001b[49m\u001b[43m_Reduction\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_enum\u001b[49m\u001b[43m(\u001b[49m\u001b[43mreduction\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3484\u001b[0m \u001b[43m \u001b[49m\u001b[43mignore_index\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3485\u001b[0m \u001b[43m \u001b[49m\u001b[43mlabel_smoothing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3486\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n",
"\u001b[0;31mOutOfMemoryError\u001b[0m: CUDA out of memory. Tried to allocate 194.00 MiB. GPU 0 has a total capacity of 3.80 GiB of which 93.19 MiB is free. Including non-PyTorch memory, this process has 3.10 GiB memory in use. Of the allocated memory 2.92 GiB is allocated by PyTorch, and 103.24 MiB is reserved by PyTorch but unallocated. If reserved but unallocated memory is large try setting PYTORCH_CUDA_ALLOC_CONF=expandable_segments:True to avoid fragmentation. See documentation for Memory Management (https://pytorch.org/docs/stable/notes/cuda.html#environment-variables)"
]
}
],
"source": [
"trainer.train()"
]
}
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